Snippet Policy Network for Multi-class Varied-length ECG Early Classification
This addresses the need for early arrhythmia detection to aid in prevention and diagnosis, offering a novel approach for varied-length time series classification, though it appears incremental in applying reinforcement learning to a specific domain.
The paper tackles the problem of early classification of cardiovascular diseases from varied-length ECG data, proposing a deep reinforcement learning framework called Snippet Policy Network (SPN) that achieves over 80% accuracy and at least 7% improvement over state-of-the-art methods in metrics like precision and F1-score.
Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases. The prevailing studies formulate arrhythmia detection from ECG as a time series classification problem. Meanwhile, early detection of arrhythmia presents a real-world demand for early prevention and diagnosis. In this paper, we address a problem of cardiovascular disease early classification, which is a varied-length and long-length time series early classification problem as well. For solving this problem, we propose a deep reinforcement learning-based framework, namely Snippet Policy Network (SPN), consisting of four modules, snippet generator, backbone network, controlling agent, and discriminator. Comparing to the existing approaches, the proposed framework features flexible input length, solves the dual-optimization solution of the earliness and accuracy goals. Experimental results demonstrate that SPN achieves an excellent performance of over 80\% in terms of accuracy. Compared to the state-of-the-art methods, at least 7% improvement on different metrics, including the precision, recall, F1-score, and harmonic mean, is delivered by the proposed SPN. To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data. Based on these excellent features from SPN, it offers a good exemplification for addressing all kinds of varied-length time series early classification problems.